An integral part of semiconductor wafer fabrication is detection of defects that lead to reduced performance of the die where the defect is located. A number of methods for performing such detection are known in the art. The methods usually include optical and/or charged particle scanning of the wafer, and analysis of the scanned image. One of the methods for detecting defects uses comparison of the image with other images, typically on a die-to-die basis and/or on a wafer-to-wafer basis, so that regions of the wafer which may have defects can be identified. Other methods are known in the art.
One of the problems of defect identification is that identified defects may in fact not lead to reduced performance of the die. For example, the existence of metal grains in the die, of a rough edge on a conductor, or of anomalies under a scanned layer, typically do not reduce performance. It is thus useful to classify defects, and to use the classification to reduce the number of defects which are considered problematic.
U.S. Pat. No. 5,966,459 to Chen, et al., whose disclosure is incorporated herein by reference, describes a method for determining classification codes for semiconductor wafer defects, and for storing the information used to determine the classification codes. A wafer is scanned after a first and subsequent manufacturing processes. After each scan images of selected defects of the wafer are examined, and are assigned a code. The code is modified according to the results of the subsequent scans.
U.S. Pat. No. 5,978,501 to Badger, et al., whose disclosure is incorporated herein by reference, describes a system for detecting defects in the design of a photolithographic mask or of a semiconductor wafer. The system derives an adaptive inspection algorithm that is claimed to allow for a tighter inspection of a mask or a wafer to a data set which has repeatable differences. The inspection is also claimed to allow flexibility in removal of unimportant differences while maintaining a tight inspection capability.
U.S. Pat. No. 6,483,938 to Hennessey, et al., whose disclosure is incorporated herein by reference, describes a system for generating a knowledge base for use in labeling anomalies on a manufactured object. A pixel-based representation of an image having an anomaly is decomposed into primitives. The anomaly is isolated, and is compared with primitives of known anomalies to locate the closest primitive set. A label of the set is presented to an operator using the system.
U.S. Pat. No. 6,487,307 to Hennessey, et al., whose disclosure is incorporated herein by reference, describes a system for optically inspecting structures on an object on a moving platform. Structure edges within the object are delineated, and a sequence of images of the object are captured. The structure is detected in each image, and a histogram is produced for each image identifying the slope and length of each edge of the structure. The histograms are used to reduce differences between images and are claimed to be able to detect foreign objects and other defects in the object.
U.S. Pat. No. 6,701,004 to Shykind, et al., whose disclosure is incorporated herein by reference, describes a method for detecting defects on a photomask by patterning alternating dice on a wafer with different process conditions. The different conditions, such as a length of exposure time and an optical focus condition, are configured to highlight and detect defect areas.
U.S. Patent Application 2004/0028276 to Okuda, et al., whose disclosure is incorporated herein by reference, describes an automatic defect classifying system. A user defines a classifying class arrangement by combining classes supplied by the system itself or classes defined by the user. The user also provides the system with a priori knowledge on the defect class, the knowledge being used as a restriction so as to carry out restricted learning.